class_docstr: Learner estimator
  
  Wraps an estimator in a cross-validation strategy.
  
  Parameters
  __________
  estimator: obj
      estimator to construct learner from.
  
  indexer: str, obj
      a cross-validation strategy. Either a :mod`mlens.index` indexer
      instance or a string.
  
  name: str, optional
      name of learner. If ``preprocessing`` is not ``None``,
      the name of the transformer will be prepended. If not specified, the
      name of the learner will be the name of the estimator in lower case.
  
  scorer: func, optional
      function to use for scoring predictions during cross-validated
      fitting.
  
  verbose: bool, int (default = False)
      whether to report completed fits.
  
  backend: str (default='threading')
      parallel backend. One of ``'treading'``, ``'multiprocessing'`` and
      ``'sequential'``.
  
  n_jobs: int (default=-1)
      degree of concurrency. Set to ``-1`` for maximum and ``1`` for
      sequential processing.
docstr: Build backend
docstr_tok: ["Build","backend"]
code: def _build(self):
      """Build backend"""
      lr_kwargs = {
          "attr": "predict",
          "verbose": self.verbose,
          "scorer": self.scorer,
          "backend": self.backend,
          "n_jobs": self.n_jobs,
          "dtype": self.dtype,
      }
  
      idx = clone(self.indexer)
      est = clone(self.estimator)
      group = make_group(idx, est, None, learner_kwargs=lr_kwargs)
  
      self._backend = group.learners[0]
      self._store_static_params()
  
imports: from ..parallel import Layer, make_group
  from ..parallel.base import ParamMixin
  from ..parallel.wrapper import EstimatorMixin
  from ..parallel._base_functions import check_stack
  from ..externals.sklearn.base import BaseEstimator
  from ..externals.sklearn.base import clone
label_desc: Use image node_id 2 for calling the LearnerEstimator obj's underlying member method code with example usage: obj._build() without return types